Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations18137
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory112.0 B

Variable types

Categorical2
Numeric11

Alerts

10 km Czas is highly overall correlated with 10 km Tempo and 8 other fieldsHigh correlation
10 km Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
15 km Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
15 km Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
20 km Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
20 km Tempo is highly overall correlated with 10 km Czas and 9 other fieldsHigh correlation
5 km Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
5 km Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
Kategoria wiekowa is highly overall correlated with PłećHigh correlation
Płeć is highly overall correlated with Kategoria wiekowaHigh correlation
Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
Tempo Stabilność is highly overall correlated with 20 km TempoHigh correlation

Reproduction

Analysis started2025-11-12 18:01:08.461388
Analysis finished2025-11-12 18:01:58.427023
Duration49.97 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Płeć
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.4 KiB
M
12896 
K
5241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18137
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M12896
71.1%
K5241
28.9%

Length

2025-11-12T19:01:58.669629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-12T19:01:59.021391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
m12896
71.1%
k5241
28.9%

Most occurring characters

ValueCountFrequency (%)
M12896
71.1%
K5241
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter18137
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M12896
71.1%
K5241
28.9%

Most occurring scripts

ValueCountFrequency (%)
Latin18137
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M12896
71.1%
K5241
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII18137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M12896
71.1%
K5241
28.9%

Kategoria wiekowa
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size283.4 KiB
M40
4336 
M30
4165 
M20
2360 
K30
1857 
K40
1790 
Other values (8)
3629 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54411
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM30
2nd rowM20
3rd rowM40
4th rowM20
5th rowM30

Common Values

ValueCountFrequency (%)
M404336
23.9%
M304165
23.0%
M202360
13.0%
K301857
10.2%
K401790
9.9%
M501450
 
8.0%
K201099
 
6.1%
M60511
 
2.8%
K50416
 
2.3%
K6073
 
0.4%
Other values (3)80
 
0.4%

Length

2025-11-12T19:01:59.279369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m404336
23.9%
m304165
23.0%
m202360
13.0%
k301857
10.2%
k401790
9.9%
m501450
 
8.0%
k201099
 
6.1%
m60511
 
2.8%
k50416
 
2.3%
k6073
 
0.4%
Other values (3)80
 
0.4%

Most occurring characters

ValueCountFrequency (%)
018137
33.3%
M12896
23.7%
46126
 
11.3%
36022
 
11.1%
K5241
 
9.6%
23459
 
6.4%
51866
 
3.4%
6584
 
1.1%
778
 
0.1%
82
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36274
66.7%
Uppercase Letter18137
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018137
50.0%
46126
 
16.9%
36022
 
16.6%
23459
 
9.5%
51866
 
5.1%
6584
 
1.6%
778
 
0.2%
82
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M12896
71.1%
K5241
28.9%

Most occurring scripts

ValueCountFrequency (%)
Common36274
66.7%
Latin18137
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
018137
50.0%
46126
 
16.9%
36022
 
16.6%
23459
 
9.5%
51866
 
5.1%
6584
 
1.6%
778
 
0.2%
82
 
< 0.1%
Latin
ValueCountFrequency (%)
M12896
71.1%
K5241
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII54411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018137
33.3%
M12896
23.7%
46126
 
11.3%
36022
 
11.1%
K5241
 
9.6%
23459
 
6.4%
51866
 
3.4%
6584
 
1.1%
778
 
0.1%
82
 
< 0.1%

5 km Czas
Real number (ℝ)

High correlation 

Distinct1253
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1668.3998
Minimum0
Maximum3825
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:01:59.784494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1283
Q11502
median1659
Q31830
95-th percentile2073
Maximum3825
Range3825
Interquartile range (IQR)328

Descriptive statistics

Standard deviation237.65566
Coefficient of variation (CV)0.14244527
Kurtosis0.29198834
Mean1668.3998
Median Absolute Deviation (MAD)163
Skewness0.15290238
Sum30259767
Variance56480.214
MonotonicityNot monotonic
2025-11-12T19:02:00.679336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156453
 
0.3%
164448
 
0.3%
168245
 
0.2%
152945
 
0.2%
164043
 
0.2%
159543
 
0.2%
170242
 
0.2%
160942
 
0.2%
159642
 
0.2%
163742
 
0.2%
Other values (1243)17692
97.5%
ValueCountFrequency (%)
01
< 0.1%
9071
< 0.1%
9231
< 0.1%
9371
< 0.1%
9471
< 0.1%
9691
< 0.1%
9711
< 0.1%
9721
< 0.1%
9761
< 0.1%
9901
< 0.1%
ValueCountFrequency (%)
38251
< 0.1%
31521
< 0.1%
25271
< 0.1%
25261
< 0.1%
24721
< 0.1%
24581
< 0.1%
24321
< 0.1%
24231
< 0.1%
24221
< 0.1%
24191
< 0.1%

5 km Tempo
Real number (ℝ)

High correlation 

Distinct1253
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5613326
Minimum0
Maximum12.75
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:01.008347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2766667
Q15.0066667
median5.53
Q36.1
95-th percentile6.91
Maximum12.75
Range12.75
Interquartile range (IQR)1.0933333

Descriptive statistics

Standard deviation0.79218554
Coefficient of variation (CV)0.14244527
Kurtosis0.29198834
Mean5.5613326
Median Absolute Deviation (MAD)0.54333333
Skewness0.15290238
Sum100865.89
Variance0.62755793
MonotonicityNot monotonic
2025-11-12T19:02:01.579353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.21333333353
 
0.3%
5.4848
 
0.3%
5.60666666745
 
0.2%
5.09666666745
 
0.2%
5.46666666743
 
0.2%
5.31666666743
 
0.2%
5.67333333342
 
0.2%
5.36333333342
 
0.2%
5.3242
 
0.2%
5.45666666742
 
0.2%
Other values (1243)17692
97.5%
ValueCountFrequency (%)
01
< 0.1%
3.0233333331
< 0.1%
3.0766666671
< 0.1%
3.1233333331
< 0.1%
3.1566666671
< 0.1%
3.231
< 0.1%
3.2366666671
< 0.1%
3.241
< 0.1%
3.2533333331
< 0.1%
3.31
< 0.1%
ValueCountFrequency (%)
12.751
< 0.1%
10.506666671
< 0.1%
8.4233333331
< 0.1%
8.421
< 0.1%
8.241
< 0.1%
8.1933333331
< 0.1%
8.1066666671
< 0.1%
8.0766666671
< 0.1%
8.0733333331
< 0.1%
8.0633333331
< 0.1%

10 km Czas
Real number (ℝ)

High correlation 

Distinct2359
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3330.4991
Minimum1853
Maximum5819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:02.036951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1853
5-th percentile2551
Q12984
median3306
Q33652
95-th percentile4178
Maximum5819
Range3966
Interquartile range (IQR)668

Descriptive statistics

Standard deviation486.64514
Coefficient of variation (CV)0.14611778
Kurtosis-0.23069542
Mean3330.4991
Median Absolute Deviation (MAD)333
Skewness0.19028606
Sum60405263
Variance236823.49
MonotonicityNot monotonic
2025-11-12T19:02:02.681763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
332829
 
0.2%
330027
 
0.1%
331727
 
0.1%
331026
 
0.1%
321026
 
0.1%
291525
 
0.1%
327125
 
0.1%
323724
 
0.1%
322024
 
0.1%
334424
 
0.1%
Other values (2349)17880
98.6%
ValueCountFrequency (%)
18531
 
< 0.1%
18801
 
< 0.1%
18851
 
< 0.1%
19301
 
< 0.1%
19501
 
< 0.1%
19551
 
< 0.1%
19652
< 0.1%
19661
 
< 0.1%
19751
 
< 0.1%
19773
< 0.1%
ValueCountFrequency (%)
58191
< 0.1%
49591
< 0.1%
49441
< 0.1%
49421
< 0.1%
49171
< 0.1%
48511
< 0.1%
48481
< 0.1%
48461
< 0.1%
48141
< 0.1%
48031
< 0.1%

10 km Tempo
Real number (ℝ)

High correlation 

Distinct1344
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5403312
Minimum3.11
Maximum10.406667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:03.132932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.11
5-th percentile4.2133333
Q14.9333333
median5.4766667
Q36.08
95-th percentile7.0533333
Maximum10.406667
Range7.2966667
Interquartile range (IQR)1.1466667

Descriptive statistics

Standard deviation0.85022493
Coefficient of variation (CV)0.15346103
Kurtosis-0.094454819
Mean5.5403312
Median Absolute Deviation (MAD)0.57
Skewness0.31915672
Sum100484.99
Variance0.72288243
MonotonicityNot monotonic
2025-11-12T19:02:03.430518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.36666666748
 
0.3%
5.48333333345
 
0.2%
5.50666666745
 
0.2%
5.3643
 
0.2%
5.62666666741
 
0.2%
5.27333333341
 
0.2%
5.42666666740
 
0.2%
5.30666666740
 
0.2%
5.42333333340
 
0.2%
5.31666666740
 
0.2%
Other values (1334)17714
97.7%
ValueCountFrequency (%)
3.111
< 0.1%
3.1533333331
< 0.1%
3.161
< 0.1%
3.1966666671
< 0.1%
3.2333333332
< 0.1%
3.251
< 0.1%
3.2666666672
< 0.1%
3.272
< 0.1%
3.2766666671
< 0.1%
3.2833333331
< 0.1%
ValueCountFrequency (%)
10.406666671
< 0.1%
9.5333333331
< 0.1%
8.9733333331
< 0.1%
8.4633333331
< 0.1%
8.3633333331
< 0.1%
8.2933333331
< 0.1%
8.2833333331
< 0.1%
8.1666666672
< 0.1%
8.151
< 0.1%
8.1466666671
< 0.1%

15 km Czas
Real number (ℝ)

High correlation 

Distinct3466
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5090.4674
Minimum2868
Maximum7697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:04.043468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2868
5-th percentile3890
Q14546
median5038
Q35588
95-th percentile6441.2
Maximum7697
Range4829
Interquartile range (IQR)1042

Descriptive statistics

Standard deviation764.14724
Coefficient of variation (CV)0.15011337
Kurtosis-0.25753045
Mean5090.4674
Median Absolute Deviation (MAD)519
Skewness0.23765622
Sum92325807
Variance583921
MonotonicityNot monotonic
2025-11-12T19:02:04.778217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
497819
 
0.1%
498919
 
0.1%
506019
 
0.1%
468518
 
0.1%
507318
 
0.1%
509717
 
0.1%
492417
 
0.1%
492817
 
0.1%
497317
 
0.1%
505017
 
0.1%
Other values (3456)17959
99.0%
ValueCountFrequency (%)
28681
 
< 0.1%
28891
 
< 0.1%
28981
 
< 0.1%
29291
 
< 0.1%
29712
< 0.1%
29741
 
< 0.1%
29873
< 0.1%
29901
 
< 0.1%
29942
< 0.1%
29971
 
< 0.1%
ValueCountFrequency (%)
76971
< 0.1%
74161
< 0.1%
74121
< 0.1%
74031
< 0.1%
73871
< 0.1%
73851
< 0.1%
73831
< 0.1%
73641
< 0.1%
73421
< 0.1%
72981
< 0.1%

15 km Tempo
Real number (ℝ)

High correlation 

Distinct1492
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8665608
Minimum3.2933333
Maximum9.8866667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:05.331544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.2933333
5-th percentile4.4266667
Q15.1866667
median5.7566667
Q36.4666667
95-th percentile7.6266667
Maximum9.8866667
Range6.5933333
Interquartile range (IQR)1.28

Descriptive statistics

Standard deviation0.9584157
Coefficient of variation (CV)0.16336926
Kurtosis-0.040567798
Mean5.8665608
Median Absolute Deviation (MAD)0.63
Skewness0.43312345
Sum106401.81
Variance0.91856066
MonotonicityNot monotonic
2025-11-12T19:02:05.868653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.76666666744
 
0.2%
5.7140
 
0.2%
5.72666666740
 
0.2%
5.6840
 
0.2%
5.72333333339
 
0.2%
5.51666666739
 
0.2%
5.48666666739
 
0.2%
5.6638
 
0.2%
5.54333333338
 
0.2%
5.9437
 
0.2%
Other values (1482)17743
97.8%
ValueCountFrequency (%)
3.2933333331
< 0.1%
3.3033333331
< 0.1%
3.331
< 0.1%
3.3366666671
< 0.1%
3.3633333331
< 0.1%
3.3766666671
< 0.1%
3.3866666671
< 0.1%
3.392
< 0.1%
3.4033333331
< 0.1%
3.4066666672
< 0.1%
ValueCountFrequency (%)
9.8866666671
< 0.1%
9.881
< 0.1%
9.8666666671
< 0.1%
9.8266666671
< 0.1%
9.4066666671
< 0.1%
9.3566666671
< 0.1%
9.1633333331
< 0.1%
9.071
< 0.1%
9.061
< 0.1%
9.0133333331
< 0.1%

20 km Czas
Real number (ℝ)

High correlation 

Distinct4631
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6981.5796
Minimum3940
Maximum10115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:06.442550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3940
5-th percentile5304
Q16211
median6872
Q37685
95-th percentile8946
Maximum10115
Range6175
Interquartile range (IQR)1474

Descriptive statistics

Standard deviation1087.7392
Coefficient of variation (CV)0.15580131
Kurtosis-0.23654417
Mean6981.5796
Median Absolute Deviation (MAD)725
Skewness0.30495031
Sum1.2662491 × 108
Variance1183176.6
MonotonicityNot monotonic
2025-11-12T19:02:06.967951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
679619
 
0.1%
684917
 
0.1%
680416
 
0.1%
677515
 
0.1%
689415
 
0.1%
665115
 
0.1%
689015
 
0.1%
671814
 
0.1%
674814
 
0.1%
647714
 
0.1%
Other values (4621)17983
99.2%
ValueCountFrequency (%)
39401
< 0.1%
39651
< 0.1%
39921
< 0.1%
40141
< 0.1%
40191
< 0.1%
40331
< 0.1%
40471
< 0.1%
40481
< 0.1%
40542
< 0.1%
40581
< 0.1%
ValueCountFrequency (%)
101151
< 0.1%
100541
< 0.1%
100271
< 0.1%
100251
< 0.1%
100171
< 0.1%
100141
< 0.1%
100111
< 0.1%
100081
< 0.1%
99922
< 0.1%
99911
< 0.1%

20 km Tempo
Real number (ℝ)

High correlation 

Distinct1773
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3037073
Minimum3.3933333
Maximum14.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:07.525983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.3933333
5-th percentile4.65
Q15.4866667
median6.1266667
Q36.98
95-th percentile8.49
Maximum14.94
Range11.546667
Interquartile range (IQR)1.4933333

Descriptive statistics

Standard deviation1.1652933
Coefficient of variation (CV)0.18485841
Kurtosis0.58765538
Mean6.3037073
Median Absolute Deviation (MAD)0.72666667
Skewness0.6914258
Sum114330.34
Variance1.3579086
MonotonicityNot monotonic
2025-11-12T19:02:08.135341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5536
 
0.2%
5.83333333336
 
0.2%
5.79666666735
 
0.2%
5.73333333335
 
0.2%
5.9334
 
0.2%
5.80666666734
 
0.2%
5.77666666733
 
0.2%
5.6833
 
0.2%
6.3933
 
0.2%
5.9833
 
0.2%
Other values (1763)17795
98.1%
ValueCountFrequency (%)
3.3933333331
 
< 0.1%
3.441
 
< 0.1%
3.4866666671
 
< 0.1%
3.5333333332
< 0.1%
3.5733333331
 
< 0.1%
3.5833333333
< 0.1%
3.5866666672
< 0.1%
3.592
< 0.1%
3.6166666671
 
< 0.1%
3.6566666671
 
< 0.1%
ValueCountFrequency (%)
14.941
< 0.1%
13.473333331
< 0.1%
12.251
< 0.1%
11.883333331
< 0.1%
11.696666671
< 0.1%
11.673333331
< 0.1%
11.266666671
< 0.1%
11.156666671
< 0.1%
11.103333331
< 0.1%
11.041
< 0.1%

Tempo Stabilność
Real number (ℝ)

High correlation 

Distinct7204
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.051067075
Minimum-0.34533333
Maximum0.62953333
Zeros1
Zeros (%)< 0.1%
Negative1232
Negative (%)6.8%
Memory size283.4 KiB
2025-11-12T19:02:08.639547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.34533333
5-th percentile-0.0039333333
Q10.020266667
median0.041466667
Q30.071866667
95-th percentile0.13968
Maximum0.62953333
Range0.97486667
Interquartile range (IQR)0.0516

Descriptive statistics

Standard deviation0.046321455
Coefficient of variation (CV)0.90707086
Kurtosis5.7298327
Mean0.051067075
Median Absolute Deviation (MAD)0.024333333
Skewness1.4740029
Sum926.20353
Variance0.0021456772
MonotonicityNot monotonic
2025-11-12T19:02:09.797928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.026216
 
0.1%
0.0256666666715
 
0.1%
0.0171333333315
 
0.1%
0.026615
 
0.1%
0.0144666666715
 
0.1%
0.0368666666715
 
0.1%
0.0313333333314
 
0.1%
0.0285333333314
 
0.1%
0.0283333333313
 
0.1%
0.0246666666713
 
0.1%
Other values (7194)17992
99.2%
ValueCountFrequency (%)
-0.34533333331
< 0.1%
-0.12781
< 0.1%
-0.11761
< 0.1%
-0.10233333331
< 0.1%
-0.10213333331
< 0.1%
-0.1021
< 0.1%
-0.087666666671
< 0.1%
-0.083066666671
< 0.1%
-0.075866666671
< 0.1%
-0.07541
< 0.1%
ValueCountFrequency (%)
0.62953333331
< 0.1%
0.52893333331
< 0.1%
0.43613333331
< 0.1%
0.42593333331
< 0.1%
0.42193333331
< 0.1%
0.39661
< 0.1%
0.39513333331
< 0.1%
0.37393333331
< 0.1%
0.36381
< 0.1%
0.3221
< 0.1%

Czas
Real number (ℝ)

High correlation 

Distinct4829
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7366.4337
Minimum4184
Maximum10551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:10.092795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4184
5-th percentile5593.8
Q16548
median7238
Q38111
95-th percentile9451.4
Maximum10551
Range6367
Interquartile range (IQR)1563

Descriptive statistics

Standard deviation1154.0151
Coefficient of variation (CV)0.15665859
Kurtosis-0.23500368
Mean7366.4337
Median Absolute Deviation (MAD)769
Skewness0.31213683
Sum1.3360501 × 108
Variance1331750.9
MonotonicityNot monotonic
2025-11-12T19:02:10.331694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
782316
 
0.1%
718316
 
0.1%
710414
 
0.1%
752114
 
0.1%
692514
 
0.1%
728514
 
0.1%
700513
 
0.1%
715013
 
0.1%
720213
 
0.1%
697913
 
0.1%
Other values (4819)17997
99.2%
ValueCountFrequency (%)
41841
< 0.1%
42051
< 0.1%
42161
< 0.1%
42181
< 0.1%
42271
< 0.1%
42341
< 0.1%
42481
< 0.1%
42771
< 0.1%
42781
< 0.1%
42842
< 0.1%
ValueCountFrequency (%)
105512
< 0.1%
105492
< 0.1%
105471
< 0.1%
105431
< 0.1%
105411
< 0.1%
105401
< 0.1%
105392
< 0.1%
105371
< 0.1%
105351
< 0.1%
105331
< 0.1%

Tempo
Real number (ℝ)

High correlation 

Distinct4829
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8200472
Minimum3.3056807
Maximum8.3360986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.4 KiB
2025-11-12T19:02:10.939394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.3056807
5-th percentile4.4195307
Q15.1734218
median5.7185747
Q36.4083116
95-th percentile7.4673303
Maximum8.3360986
Range5.030418
Interquartile range (IQR)1.2348898

Descriptive statistics

Standard deviation0.91176038
Coefficient of variation (CV)0.15665859
Kurtosis-0.23500368
Mean5.8200472
Median Absolute Deviation (MAD)0.60756893
Skewness0.31213683
Sum105558.2
Variance0.831307
MonotonicityNot monotonic
2025-11-12T19:02:11.280604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.18076953516
 
0.1%
5.67512048716
 
0.1%
5.61270443214
 
0.1%
5.9421663914
 
0.1%
5.47128071414
 
0.1%
5.75570830414
 
0.1%
5.53448684513
 
0.1%
5.64904795813
 
0.1%
5.69013194313
 
0.1%
5.51394485313
 
0.1%
Other values (4819)17997
99.2%
ValueCountFrequency (%)
3.3056806511
< 0.1%
3.322272261
< 0.1%
3.3309631031
< 0.1%
3.3325432571
< 0.1%
3.3396539461
< 0.1%
3.3451844831
< 0.1%
3.3562455561
< 0.1%
3.3791577781
< 0.1%
3.3799478551
< 0.1%
3.3846883152
< 0.1%
ValueCountFrequency (%)
8.3360986022
< 0.1%
8.3345184482
< 0.1%
8.3329382951
< 0.1%
8.3297779881
< 0.1%
8.3281978351
< 0.1%
8.3274077591
< 0.1%
8.3266176822
< 0.1%
8.3250375291
< 0.1%
8.3234573751
< 0.1%
8.3218772221
< 0.1%

Interactions

2025-11-12T19:01:52.582668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:09.811966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:12.643379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:14.950856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:18.383208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:28.471409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:32.919904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:36.906627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:40.748636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:44.088260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:47.262847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:52.883810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:10.078309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:12.876374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:15.200943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:18.591207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:28.908781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:33.131107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:37.389063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:41.061179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:44.374987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:47.506034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:53.471326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:10.407240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:13.091459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:15.409969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:18.993486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:29.369076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:33.479008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:37.967847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:41.291056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:44.577584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:48.104627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:53.823713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:10.723494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:13.311072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:15.617188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:19.594984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:30.022901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:33.929797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:38.168429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:41.511154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:45.168556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:48.369361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:54.306062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:10.977132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:13.525673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:15.836148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:20.057179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:30.571344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:34.141050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:38.366101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:42.078036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:45.384652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:48.924840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:54.530452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:11.200155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:13.721374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:16.024227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:20.578587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:31.037471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:34.422762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:38.684756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:42.482242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:45.728927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:49.408075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:54.854529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:11.492901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:13.928745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:16.364149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:21.010044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:31.366933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:34.867811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:39.138703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:42.863027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:45.959976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:49.992636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:55.354817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:11.728363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:14.131103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:16.657962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:26.554900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:31.598218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:35.329066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:39.618654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:43.070276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:46.169521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:50.475558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:55.705618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:11.971356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:14.352916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:16.975506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:26.986784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:31.790972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:35.782383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:39.908943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:43.267071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:46.511766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:51.077101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:56.071930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:12.187961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:14.535096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:17.806008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:27.593106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:32.051218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:36.175686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:40.131000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:43.630842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:46.701660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:51.400748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:56.610015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:12.421524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:14.741473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:18.193834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:28.035553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:32.567260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:36.491055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:40.375479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:43.851582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:47.009165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-12T19:01:52.064466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2025-11-12T19:02:11.506816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
10 km Czas10 km Tempo15 km Czas15 km Tempo20 km Czas20 km Tempo5 km Czas5 km TempoCzasKategoria wiekowaPłećTempoTempo Stabilność
10 km Czas1.0000.9920.9930.9540.9730.8740.9890.9890.9710.1320.3420.9710.288
10 km Tempo0.9921.0000.9940.9690.9820.9000.9630.9630.9790.1250.3410.9790.359
15 km Czas0.9930.9941.0000.9820.9900.9100.9730.9730.9890.1260.3420.9890.368
15 km Tempo0.9540.9690.9821.0000.9890.9450.9200.9200.9900.1200.3260.9900.492
20 km Czas0.9730.9820.9900.9891.0000.9570.9450.9450.9990.1200.3270.9990.473
20 km Tempo0.8740.9000.9100.9450.9571.0000.8300.8300.9570.1010.2720.9570.680
5 km Czas0.9890.9630.9730.9200.9450.8301.0001.0000.9430.1270.3110.9430.206
5 km Tempo0.9890.9630.9730.9200.9450.8301.0001.0000.9430.1270.3110.9430.206
Czas0.9710.9790.9890.9900.9990.9570.9430.9431.0000.1190.3241.0000.477
Kategoria wiekowa0.1320.1250.1260.1200.1200.1010.1270.1270.1191.0001.0000.1190.038
Płeć0.3420.3410.3420.3260.3270.2720.3110.3110.3241.0001.0000.3240.079
Tempo0.9710.9790.9890.9900.9990.9570.9430.9431.0000.1190.3241.0000.477
Tempo Stabilność0.2880.3590.3680.4920.4730.6800.2060.2060.4770.0380.0790.4771.000

Missing values

2025-11-12T19:01:57.221781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-12T19:01:57.938456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PłećKategoria wiekowa5 km Czas5 km Tempo10 km Czas10 km Tempo15 km Czas15 km Tempo20 km Czas20 km TempoTempo StabilnośćCzasTempo
0MM30971.03.2366671930.03.1966672929.03.3300004014.03.6166670.0254674216.03.330963
1MM20972.03.2400001955.03.2766672971.03.3866674047.03.5866670.0230004227.03.339654
2MM40969.03.2300001950.03.2700002971.03.4033334048.03.5900000.0242674234.03.345184
3MM20937.03.1233331885.03.1600002898.03.3766674058.03.8666670.0489334278.03.379948
4MM30990.03.3000001975.03.2833332997.03.4066674098.03.6700000.0246674302.03.398910
5MM201030.03.4333332063.03.4433333131.03.5600004263.03.7733330.0227334456.03.520581
6MM301013.03.3766672035.03.4066673099.03.5466674250.03.8366670.0304004462.03.525322
7MM201029.03.4300002063.03.4466673131.03.5600004282.03.8366670.0266674483.03.541914
8MM301035.03.4500002063.03.4266673133.03.5666674284.03.8366670.0260004490.03.547444
9MM201035.03.4500002063.03.4266673133.03.5666674290.03.8566670.0272004496.03.552185
PłećKategoria wiekowa5 km Czas5 km Tempo10 km Czas10 km Tempo15 km Czas15 km Tempo20 km Czas20 km TempoTempo StabilnośćCzasTempo
19012KK302395.07.9833334777.07.9400007275.08.3266679931.08.8533330.05993310528.08.317927
19013MM402398.07.9933334778.07.9333337272.08.3133339933.08.8700000.06020010530.08.319507
19014KK402419.08.0633334846.08.0900007383.08.4566679948.08.5500000.03653310535.08.323457
19015MM402145.07.1500004418.07.5766677139.09.0700009913.09.2466670.15566710537.08.325038
19017KK402422.08.0733334848.08.0866677385.08.4566679952.08.5566670.03640010539.08.326618
19018KK402423.08.0766674851.08.0933337387.08.4533339953.08.5533330.03580010540.08.327408
19019MM402147.07.1566674497.07.8333337111.08.7133339896.09.2833330.14520010547.08.332938
19020KK302266.07.5533334754.08.2933337364.08.70000010008.08.8133330.08373310549.08.334518
19021KK402153.07.1766674499.07.8200007113.08.7133339899.09.2866670.14446710549.08.334518
19022KK402308.07.6933334749.08.1366677254.08.3500009941.08.9566670.08006710551.08.336099